Genkit Dart

Genkit Dart is an AI SDK for Dart that provides a unified interface for code generation, structured outputs, tools, flows, and AI agents.

Core Features and Usage

If you need help with initializing Genkit (Genkit()), Generation (ai.generate), Tooling (ai.defineTool), Flows (ai.defineFlow), Embeddings (ai.embedMany), streaming, or calling remote flow endpoints, please load the core framework reference: references/genkit.md

Prompts (Dotprompt)

.prompt files keep prompt content out of Dart code with YAML frontmatter plus a Handlebars template. See references/dotprompt.md: promptDir, ai.prompt() (call/stream/render), variants, partials, named schemas via defineSchema, and the tools/maxTurns/returnToolRequests/use (middleware) frontmatter fields. A .prompt file can also back an agent directly via definePromptAgent.

Agents

Genkit Dart has an agent API for persistent, multi-turn conversations (sessions, snapshots, interrupts, branching, background execution, custom state, artifacts, and multi-agent delegation). The agent/session/snapshot APIs are experimental and live behind opt-in imports: server APIs come from package:genkit/experimental.dart (alongside package:genkit/genkit.dart), the browser/HTTP client from package:genkit/experimental_client.dart (alongside package:genkit/client.dart), and dart:io extras like FileSessionStore from package:genkit/experimental_io.dart. These entry points are @experimental, so importing them raises an experimental_member_use analyzer warning you can silence in analysis_options.yaml. The remoteAgent client works from any Dart app, including Flutter, and the backend is fully interchangeable — it can talk to a Genkit agent implemented in Dart, JS/TypeScript, or Go over the same HTTP protocol. A few Dart specifics: interrupts are modeled as tools that return .interrupt(...) (there is no defineInterrupt), sub-agent delegation uses the agents() middleware from package:genkit_middleware, and there is no artifacts() middleware yet (define artifact tools directly).

For more details see:

• Agents: defining/serving an agent and client-managed state (start here).
• Sessions & persistence: session stores (InMemorySessionStore/FileSessionStore/FirestoreSessionStore).
• Human-in-the-loop / interrupts: pausing for approval/input via .interrupt(...) and resuming.
• Branching: forking a conversation from a snapshot.
• Background agents: detaching long-running turns and polling.
• Working with state: typed custom session state, auto-synced to the client.
• Artifacts: producing and reading named deliverables.
• Multi-agent orchestration: delegating to sub-agents with the agents() middleware.
• Advanced custom agents: defineCustomAgent for full turn control.
• Deploying agents: serving agents over HTTP with genkit_shelf (multiple agents, CORS).

Generative UI (A2UI)

Genkit Dart has an A2UI (Agent-to-UI) plugin (genkit_a2ui) that lets an agent stream interactive UI surfaces (cards, lists, forms, buttons), not just prose. The whole server-side integration is the a2ui() model middleware in an agent's (or ai.generate's) use list; the Flutter client renders surfaces with the genui package plus the helpers in package:genkit_a2ui/client.dart. Dart specific: you must register A2uiPlugin() in Genkit(plugins: [...]) (unlike JS, middleware is resolved by name from the registry).

• A2UI: server middleware, options, Flutter/genui client rendering, user actions/forms, custom catalogs, and the security/trust boundary.

Genkit CLI (recommended)

genkit start unintrusively wraps any Dart program that uses the Genkit library, running it unchanged while capturing traces from every Genkit action so you can prove tools were actually called and inspect model I/O from the terminal, even for headless checks. It forwards stdio, so interactive CLI tools that rely on stdin/stdout work without issues. Running the app directly (dart run) skips trace capture, so you're debugging blind. Check install with genkit --version.

Installation:

curl -sL cli.genkit.dev | bash # Native CLI
# OR
npm install -g genkit-cli # Via npm
# OR run commands directly with npx without a global install (prefix every genkit command):
# npx genkit-cli start -- dart run main.dart

Primary pattern (default): prefix genkit start -- to your normal run command. This collects telemetry from any Genkit code your program runs, whether triggered from the dev UI, your own web server/web UI, or a plain script. Starts the Developer UI (usually http://localhost:4000) for running flows, model and agent playground, and browsing traces:

genkit start -- dart run main.dart
genkit start --noui -- dart run main.dart   # same, without the Dev UI (still a persistent server)

genkit start runs until you stop it with Ctrl+C. That is expected and correct for the common cases: a server your web/mobile app calls, or an interactive CLI you exit yourself. --noui only drops the Dev UI; it is not a one-shot command and will not exit on its own. Do not use genkit start as a blocking step in automated/non-interactive contexts; use flow:run (below) for that.

Non-interactive use (agents/CI): add the global --non-interactive flag before -- so the CLI uses defaults and never blocks on a prompt (e.g. the first-run analytics notice): genkit start --non-interactive -- dart run main.dart (works with flow:run too).

Run a flow (flow:run): invoke a specific flow by name from the CLI. Append your run command after -- to spin up the runtime just for this run (the command runs as-is to register your flows):

genkit flow:run myFlow '{"data": "input"}' -- dart run main.dart

This is self-terminating: it runs the flow once, prints a Trace ID, then exits, so it's the right choice for a quick, non-interactive check (unlike genkit start). Note: flow:run runs flows (ai.defineFlow), not agents; you can't flow:run an agent (ai.defineAgent) directly. To exercise an agent from the CLI, wrap one turn in a throwaway flow and run that (see Agents). Traces for this run can be inspected using the trace commands below.

Gotcha: top-level final declarations are lazy. Flows and agents defined as top-level final register with Genkit only when the symbol is first evaluated. An empty main() registers nothing, so flow:run fails with Process exited before runtime was ready. Reference the flow/agent symbols from main() (or import a module that does) so their define* calls actually run.

Debugging with traces: the fastest way to see prompts, model inputs/outputs, tool calls, latencies, and errors. Inspect from the terminal after any run under genkit start:

genkit trace:list                        # find recent trace IDs
genkit trace:get <traceId>               # full trace details (inputs, outputs, tool calls, errors)
genkit trace:get <traceId> --format json # machine-readable JSON, safe to pipe into jq or other parsers

For machine-readable output, pass --format json to get clean JSON you can pipe into jq or other parsers. The default output is human-oriented (banner/log lines, possible truncation on large traces), so don't pipe that form directly; use --format json, grep, or the Dev UI trace viewer.

Documentation:

genkit docs:search "streaming" dart
genkit docs:list dart
genkit docs:read dart/flows.md

Plugin Ecosystem

Genkit relies on a large suite of plugins to perform generative AI actions, interface with external LLMs, or host web servers.

When asked to use any given plugin, always verify usage by referring to its corresponding reference below. You should load the reference when you need to know the specific initialization arguments, tools, models, and usage patterns for the plugin:

Plugin Name	Reference Link	Description
genkit_google_genai	references/genkit_google_genai.md	Load for Google Gemini plugin interface usage.
genkit_anthropic	references/genkit_anthropic.md	Load for Anthropic plugin interface for Claude models.
genkit_openai	references/genkit_openai.md	Load for OpenAI plugin interface for GPT models, Groq, and custom compatible endpoints.
genkit_middleware	references/genkit_middleware.md	Load for Tooling for specific agentic behavior: filesystem, skills, and toolApproval interrupts.
genkit_mcp	references/genkit_mcp.md	Load for Model Context Protocol integration (Server, Host, and Client capabilities).
genkit_chrome	references/genkit_chrome.md	Load for Running Gemini Nano locally inside the Chrome browser using the Prompt API.
genkit_shelf	references/genkit_shelf.md	Load for Integrating Genkit Flow actions over HTTP using Dart Shelf.
genkit_firebase_ai	references/genkit_firebase_ai.md	Load for Firebase AI plugin interface (Gemini API via Vertex AI).
genkit_a2ui	references/a2ui.md	Load for A2UI (Agent-to-UI): streaming generative UI surfaces via the a2ui() middleware, rendered on the client with genui.

External Dependencies

Whenever you define schemas mapping inside of Tools, Flows, and Prompts, you must use the schemantic library. To learn how to use schemantic, ensure you read references/schemantic.md for how to implement type safe generated Dart code. This is particularly relevant when you encounter symbols like @Schema(), SchemanticType, or classes with the $ prefix. Genkit Dart uses schemantic for all of its data models so it's a CRITICAL skill to understand for using Genkit Dart.

Best Practices

• Agent or flow? If the task is conversational, multi-turn, or described as "an agent", "assistant", or "chatbot", build it with ai.defineAgent (see Agents) rather than hand-rolling a generate + tools loop inside a flow. Reach for a plain flow only for single-shot, stateless generation.
• Always check that code cleanly compiles using dart analyze before generating the final response.
• Always use the Genkit CLI for local development and debugging.
• Verify with traces, not a blind run. Running the app directly (dart run) does not capture dev traces. See the Genkit CLI section for how to run your app and capture traces.